Fix spelling in neural_network/convolution_neural_network.py (TheAlgorithms#849)

* Fix spelling in neural_network/convolution_neural_network.py * fix import Signed-off-by: cedric.farinazzo <[email protected]>

Author: cedricfarinazzo

neural_network/convolution_neural_network.py

@@ -14,15 +14,16 @@
  Github: [email protected]
  Date: 2017.9.20
  - - - - - -- - - - - - - - - - - - - - - - - - - - - - -
-'''
+'''
 from __future__ importprint_function
 
+importpickle
 importnumpyasnp
 importmatplotlib.pyplotasplt
 
 classCNN():
 
-def__init__(self,conv1_get,size_p1,bp_num1,bp_num2,bp_num3,rate_w=0.2,rate_t=0.2):
+def__init__(self,conv1_get,size_p1,bp_num1,bp_num2,bp_num3,rate_w=0.2,rate_t=0.2):
 '''
  :param conv1_get: [a,c,d]，size, number, step of convolution kernel
  :param size_p1: pooling size
@@ -48,32 +49,30 @@ def __init__(self,conv1_get,size_p1,bp_num1,bp_num2,bp_num3,rate_w=0.2,rate_t=0.
 self.thre_bp3=-2*np.random.rand(self.num_bp3)+1
 
 
-defsave_model(self,save_path):
+defsave_model(self,save_path):
 #save model dict with pickle
-importpickle
 model_dic={'num_bp1':self.num_bp1,
-'num_bp2':self.num_bp2,
-'num_bp3':self.num_bp3,
-'conv1':self.conv1,
-'step_conv1':self.step_conv1,
-'size_pooling1':self.size_pooling1,
-'rate_weight':self.rate_weight,
-'rate_thre':self.rate_thre,
-'w_conv1':self.w_conv1,
-'wkj':self.wkj,
-'vji':self.vji,
-'thre_conv1':self.thre_conv1,
-'thre_bp2':self.thre_bp2,
-'thre_bp3':self.thre_bp3}
+'num_bp2':self.num_bp2,
+'num_bp3':self.num_bp3,
+'conv1':self.conv1,
+'step_conv1':self.step_conv1,
+'size_pooling1':self.size_pooling1,
+'rate_weight':self.rate_weight,
+'rate_thre':self.rate_thre,
+'w_conv1':self.w_conv1,
+'wkj':self.wkj,
+'vji':self.vji,
+'thre_conv1':self.thre_conv1,
+'thre_bp2':self.thre_bp2,
+'thre_bp3':self.thre_bp3}
 withopen(save_path, 'wb') asf:
 pickle.dump(model_dic, f)
 
 print('Model saved： %s'%save_path)
 
 @classmethod
-defReadModel(cls,model_path):
+defReadModel(cls,model_path):
 #read saved model
-importpickle
 withopen(model_path, 'rb') asf:
 model_dic=pickle.load(f)
 
@@ -97,13 +96,13 @@ def ReadModel(cls,model_path):
 returnconv_ins
 
 
-defsig(self,x):
+defsig(self,x):
 return1/ (1+np.exp(-1*x))
 
-defdo_round(self,x):
+defdo_round(self,x):
 returnround(x, 3)
 
-defconvolute(self,data,convs,w_convs,thre_convs,conv_step):
+defconvolute(self,data,convs,w_convs,thre_convs,conv_step):
 #convolution process
 size_conv=convs[0]
 num_conv=convs[1]
@@ -132,7 +131,7 @@ def convolute(self,data,convs,w_convs,thre_convs,conv_step):
 focus_list=np.asarray(focus1_list)
 returnfocus_list,data_featuremap
 
-defpooling(self,featuremaps,size_pooling,type='average_pool'):
+defpooling(self,featuremaps,size_pooling,type='average_pool'):
 #pooling process
 size_map=len(featuremaps[0])
 size_pooled=int(size_map/size_pooling)
@@ -153,7 +152,7 @@ def pooling(self,featuremaps,size_pooling,type='average_pool'):
 featuremap_pooled.append(map_pooled)
 returnfeaturemap_pooled
 
-def_expand(self,datas):
+def_expand(self,datas):
 #expanding three dimension data to one dimension list
 data_expanded= []
 foriinrange(len(datas)):
@@ -164,14 +163,14 @@ def _expand(self,datas):
 data_expanded=np.asarray(data_expanded)
 returndata_expanded
 
-def_expand_mat(self,data_mat):
+def_expand_mat(self,data_mat):
 #expanding matrix to one dimension list
 data_mat=np.asarray(data_mat)
 shapes=np.shape(data_mat)
 data_expanded=data_mat.reshape(1,shapes[0]*shapes[1])
 returndata_expanded
 
-def_calculate_gradient_from_pool(self,out_map,pd_pool,num_map,size_map,size_pooling):
+def_calculate_gradient_from_pool(self,out_map,pd_pool,num_map,size_map,size_pooling):
 '''
  calcluate the gradient from the data slice of pool layer
  pd_pool: list of matrix
@@ -190,7 +189,7 @@ def _calculate_gradient_from_pool(self,out_map,pd_pool,num_map,size_map,size_poo
 pd_all.append(pd_conv2)
 returnpd_all
 
-deftrian(self,patterns,datas_train, datas_teach, n_repeat, error_accuracy,draw_e=bool):
+deftrain(self,patterns,datas_train, datas_teach, n_repeat, error_accuracy,draw_e=bool):
 #model traning
 print('----------------------Start Training-------------------------')
 print((' - - Shape: Train_Data ',np.shape(datas_train)))
@@ -206,7 +205,7 @@ def trian(self,patterns,datas_train, datas_teach, n_repeat, error_accuracy,draw_
 data_train=np.asmatrix(datas_train[p])
 data_teach=np.asarray(datas_teach[p])
 data_focus1,data_conved1=self.convolute(data_train,self.conv1,self.w_conv1,
-self.thre_conv1,conv_step=self.step_conv1)
+self.thre_conv1,conv_step=self.step_conv1)
 data_pooled1=self.pooling(data_conved1,self.size_pooling1)
 shape_featuremap1=np.shape(data_conved1)
 '''
@@ -231,7 +230,7 @@ def trian(self,patterns,datas_train, datas_teach, n_repeat, error_accuracy,draw_
 pd_conv1_pooled=pd_i_all/ (self.size_pooling1*self.size_pooling1)
 pd_conv1_pooled=pd_conv1_pooled.T.getA().tolist()
 pd_conv1_all=self._calculate_gradient_from_pool(data_conved1,pd_conv1_pooled,shape_featuremap1[0],
-shape_featuremap1[1],self.size_pooling1)
+shape_featuremap1[1],self.size_pooling1)
 #weight and threshold learning process---------
 #convolution layer
 fork_convinrange(self.conv1[1]):
@@ -268,15 +267,15 @@ def draw_error():
 draw_error()
 returnmse
 
-defpredict(self,datas_test):
+defpredict(self,datas_test):
 #model predict
 produce_out= []
 print('-------------------Start Testing-------------------------')
 print((' - - Shape: Test_Data ',np.shape(datas_test)))
 forpinrange(len(datas_test)):
 data_test=np.asmatrix(datas_test[p])
 data_focus1, data_conved1=self.convolute(data_test, self.conv1, self.w_conv1,
-self.thre_conv1, conv_step=self.step_conv1)
+self.thre_conv1, conv_step=self.step_conv1)
 data_pooled1=self.pooling(data_conved1, self.size_pooling1)
 data_bp_input=self._expand(data_pooled1)
 
@@ -289,11 +288,11 @@ def predict(self,datas_test):
 res= [list(map(self.do_round,each)) foreachinproduce_out]
 returnnp.asarray(res)
 
-defconvolution(self,data):
+defconvolution(self,data):
 #return the data of image after convoluting process so we can check it out
 data_test=np.asmatrix(data)
 data_focus1, data_conved1=self.convolute(data_test, self.conv1, self.w_conv1,
-self.thre_conv1, conv_step=self.step_conv1)
+self.thre_conv1, conv_step=self.step_conv1)
 data_pooled1=self.pooling(data_conved1, self.size_pooling1)
 
 returndata_conved1,data_pooled1
@@ -303,4 +302,4 @@ def convolution(self,data):
 pass
 '''
  I will put the example on other file
-'''
+'''